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Energy prediction in buildings plays a crucial role in effective energymanagement. Precise predictions are essential for achieving optimal energyconsumption and distribution within the grid. This paper introduces a LongShort-Term Memory (LSTM) model designed to forecast building energy consumptionusing historical energy data, occupancy patterns, and weather conditions. TheLSTM model provides accurate short, medium, and long-term energy predictionsfor residential and commercial buildings compared to existing predictionmodels. We compare our LSTM model with established prediction methods,including linear regression, decision trees, and random forest. Encouragingly,the proposed LSTM model emerges as the superior performer across all metrics.It demonstrates exceptional prediction accuracy, boasting the highest R2 scoreof 0.97 and the most favorable mean absolute error (MAE) of 0.007. Anadditional advantage of our developed model is its capacity to achieveefficient energy consumption forecasts even when trained on a limited dataset.We address concerns about overfitting (variance) and underfitting (bias)through rigorous training and evaluation on real-world data. In summary, ourresearch contributes to energy prediction by offering a robust LSTM model thatoutperforms alternative methods and operates with remarkable efficiency,generalizability, and reliability.

DECODE: Data-driven Energy Consumption Prediction leveraging Historical Data and Environmental Factors in Buildings